code: def test_resets_properly(self):
      base_env_name = "CartPole-v0"
      batch_size = 5
      reward_range = (-1, 1)
      nsteps = 100
  
      env = gym_env_problem.GymEnvProblem(
          base_env_name=base_env_name,
          batch_size=batch_size,
          reward_range=reward_range,
      )
      env.name = base_env_name
  
      num_dones = 0
      while num_dones == 0:
          env, num_dones, _ = self.play_env(
              env=env,
              nsteps=nsteps,
              batch_size=batch_size,
              reward_range=reward_range,
          )
  
      # Some completed trajectories have been generated.
      self.assertGreater(env.trajectories.num_completed_trajectories, 0)
  
      # This should clear the env completely of any state.
      env.reset()
  
      # Assert that there aren't any completed trajectories in the env now.
      self.assertEqual(env.trajectories.num_completed_trajectories, 0)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import gym
  from gym.spaces import Box
  from gym.spaces import Discrete
  import numpy
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.envs import env_problem
  from tensor2tensor.envs import env_problem_utils
  from tensor2tensor.envs import gym_env_problem
  from tensor2tensor.layers import modalities
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 11 for calling the GymEnvProblemTest obj's underlying member method code with example usage: obj.test_resets_properly() without return types